1. Field of the Invention
This invention relates to a pattern learning method for a neural network. This invention particularly relates to a pattern learning method, wherein a large number of cells of a neural network are caused to learn a large number of feature patterns.
2. Description of the Prior Art
Matching techniques have heretofore been used widely in the field of pattern recognition in image processing. One of typical matching techniques is to accumulate image pattern models (i.e., templates), which have been selected manually, as a knowledge and to carry out discrimination of a target object by template matching. However, as described in, for example, "Gazo Kaiseki Handbook" (Image Analysis Handbook) by Takagi and Shimoda, pp. 172-205, 1991, Publishing Group of the University of Tokyo, the template matching technique has various drawbacks in that, for example, models of discrimination target objects are fixed, and therefore the technique cannot cope with a wide variety of changes in the target objects (such as changes in sizes, directions, and shapes of the target objects). Also, because ordering of image patterns is not effected, if the number of models of image patterns becomes large, it is difficult to ascertain whether or not a pattern necessary for discrimination of a target object is missing.
Recently, in order to solve the problems described above, a technique utilizing a neural network, which simulates the principle of information processing carried out by a brain of a human being, has been proposed. The technique utilizing a neural network aims at carrying out the learning of models of image patterns accumulated as templates by use of a learning model of the neural network and utilizing the results of the learning operation during the discrimination of a target object. Specifically, with the technique utilizing a neural network, an attempt is made to impart the flexible characteristics of the neural network to the templates at the learning stage such that the templates can cope with a wide variety of changes in target objects.
By way of example, the learning models include Kohonen's self-organized mapping, which is described in, for example, "Self-Organization and Associative Memory" by T. Kohonen, Springer-Verlag, 1984. The Kohonen's self-organized mapping model learns topological mapping through self-organization. The topological mapping means that, for example, a signal which a human being has received from the outer world, i.e., a signal representing a certain pattern, is allocated to a neuron on the cortex in accordance with a certain kind of rule reflecting the order of the pattern.
The learning of binary images utilizing the Kohonen's self-organization has been reported in, for example, "Self-Organizing Optical Neural Network for Unsupervised Learning" by Taiwei Lu, etc., Optical Engineering, Vol. 29, No. 9, 1990. Also, utilization of the Kohonen's self-organization in rough classification pre-processing during character recognition has been reported in, for example, "Identification of JIS First and Second Level Printed Characters by Comb NET" by Toma, Iwata, et al., Nagoya Kogyo University, Autumn Collected Drafts of The Institute of Electronics and Communication Engineers of Japan, 1990.
Additionally, an attempt has been made to carry out the learning of voices by a learning vector quantization technique (LVQ), which is an advanced self-organization technique, and to discriminate voices. Such an attempt is described in, for example, "The Self-Organization Map" by T. Kohonen, Proceedings of The IE EE, Vol. 78, No. 9, 1990, 9. With this attempt, vector quantization is carried out with the self-organized mapping, and voices are thereby recognized.
However, the aforesaid techniques utilizing neural networks, such as the Kohonen's self-organization, are applied only to character patterns and simple binary images. It is difficult for such techniques to be applied to complicated gray level images, and no attempt has heretofore been made to apply such techniques to complicated gray level images.
By way of example, problems encountered when the target object to be discriminated is a human face will be described hereinbelow. As described above, with the Kohonen's self-organized mapping, a certain pattern can be learned in accordance with a certain kind of rule reflecting the order of the pattern. However, when a learning operation is carried out by using a closed human eye pattern 40 and an open eye pattern 41, which are shown in FIG. 24, as the patterns, the results of the learning operation shown in FIG. 25 are obtained. Specifically, cells located between the cells, to which the closed eye pattern 40 has been allocated, and the cells, to which the open eye pattern 41 has been allocated, learn an eye pattern 42 which results from the superposition of the closed eye pattern 40 and the open eye pattern 41. Therefore, with the superposition learning operation, it is difficult to utilize the results of the learning operation during the discrimination of a target object, and the learning operation cannot be carried out efficiently. If the feature patterns of all faces present in the world could be learned, it would be possible to carry out the discrimination of faces appropriately. However, actually, it will be impossible to learn the feature patterns of all faces present in the world.
Therefore, a need exists for an efficient learning method, with which the information representing the feature patterns of typical faces is supplied and intermediate patterns between these feature patterns can be learned from the information. Such an efficient learning method will enable discrimination (generalization) of images other than the supplied feature patterns, i.e. images which have not been learned. Specifically, templates can be imparted with flexibility such that they can cope with a wide variety of changes in target objects.